KEYWORDS: Convolutional neural networks, Cultural heritage, 3D image processing, 3D modeling, Image processing, 3D image reconstruction, Reconstruction algorithms, Photogrammetry, Photography, Data modeling
This paper deals with a virtual anastylosis of a Greek Archaic statue from ancient Sicily and the development of a public outreach protocol for those with visual impairment or cognitive disabilities through the application of three-dimensional (3-D) printing and haptic technology. The case study consists of the marble head from Leontinoi in southeastern Sicily, acquired in the 18th century and later kept in the collection of the Museum of Castello Ursino in Catania, and a marble torso, retrieved in 1904 and since then displayed in the Archaeological Museum of Siracusa. Due to similar stylistic features, the two pieces can be dated to the end of the sixth century BC. Their association has been an open problem, largely debated by scholars, who have based their hypotheses on comparisons between pictures, but the reassembly of the two artifacts was never attempted. As a result the importance of such an artifact, which could be the only intact Archaic statue of a kouros ever found in Greek Sicily, has not fully been grasped by the public. Consequently, the curatorial dissemination of the knowledge related with such artifacts is purely based on photographic material. As a response to this scenario, the two objects have been 3-D scanned and virtually reassembled. The result has been shared digitally with the public via a web platform and, in order to include increased accessibility for the public with physical or cognitive disabilities, copies of the reassembled statue have been 3-D printed and an interactive test with the 3-D model has been carried out with a haptic device.
Maria Francesca Alberghina, Filippo Alberghina, Dario Allegra, Francesco Di Paola, Laura Maniscalco, Giuseppe Milazzo, Filippo L. Milotta, Lorella Pellegrino, Salvatore Schiavone, Filippo Stanco
KEYWORDS: 3D modeling, Silver, 3D image processing, Data modeling, X-rays, 3D acquisition, Integrated modeling, Ultraviolet radiation, Diagnostics, 3D scanning
The Morgantina silver treasure belonging to the Archaeological Museum of Aidone (Sicily) was involved in a three-dimensional (3-D) survey and diagnostics campaign for monitoring the collection over time in anticipation of their temporary transfer to the Metropolitan Museum of Art in New York for a period of 4 years. Using a multidisciplinary approach, a scientific and methodological protocol based on noninvasive techniques to achieve a complete and integrated knowledge of the precious items and their conservation state, as well as to increase their valorization, has been developed. All acquired data, i.e., 3-D models, ultraviolet fluorescence, x-ray images, and chemical information, will be made available, in an integrated way, within a web-oriented platform, which will present an in-progress tool to deepen existing archaeological knowledge and production technologies and to obtain referenced information of the conservation state before and after moving of the collection from its exposure site.
The objective and repeatable measurement of the color of artifacts is a much needed practice in archeological research.
Indeed, in many cases, color information are crucial for the interpretation of cultural products. To avoid the risks of a too
subjective autoptic recognition, Munsell system is commonly adopted. This method requires that a human operator
matches the perceived color to its standardized version in Munsell Charts. This approach has significant limitations that
can mislead archaeologists in their daily work. The alternative would be the use of accurately calibrated sensors in a
controlled illumination environment. These commodities are rarely available for most of the “on field” studies. In this
paper a simple, economical, based on consumer level electronics and sensors, semi-automatic method of color detection
on accurately and precisely selected regions of digital images of ancient pottery is presented. The proposed method
indeed uses only the data from a common CCD sensor supported by a simple color measurement pipeline. Our tool is
aimed to prevent subjective errors during color identification and to speed up the process of identification itself. The
results obtained and percentages of successful matching with human Munsell color identification have statistically shown
that our proposal is an interesting starting point to develop a full, cheap, easy to use system that could facilitate some
aspects of the archaeologist’s work.
We propose a new algorithm to digitally restore vintage photographic prints affected by foxing and water blotches. It semiautomatically recovers the defects utilizing the features of the stains. The restoration process enhances the residual image information still present in the area. It is composed of three different steps: inpainting, additive-multiplicative (A-M) modeling, and interpolation.
Reconstruction techniques exploit a first building process using Low-resolution (LR) images to obtain a "draft" High Resolution (HR) image and then update the estimated HR by back-projection error reduction. This paper presents different HR draft image construction techniques and shows methods providing the best solution in terms of final perceived/measured quality. The following algorithms have been analysed: a proprietary Resolution Enhancement method (RE-ST); a Locally Adaptive Zooming Algorithm (LAZA); a Smart Interpolation by Anisotropic Diffusion (SIAD); a Directional Adaptive Edge-Interpolation (DAEI); a classical Bicubic interpolation and a Nearest Neighbour algorithm. The resulting HR images are obtained by merging the zoomed LR-pictures using two different strategies: average or median. To improve the corresponding HR images two adaptive error reduction techniques are applied in the last step: auto-iterative and uncertainty-reduction.
This paper introduces a method for the automatic discrimination of digital images based on their semantic content. The proposed system allows to detect if a digital image contains or not a text. This is realized by a multi-steps procedure based on low-level features set properly derived. Our experiments show that the proposed algorithm is competitive in efficiency with classical techniques, and it has a lower complexity.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.